Correction Of Segmentation Errors Research Articles

Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method

ABSTRACT In recent years, the deep learning-based semantic segmentation for point clouds has demonstrated remarkable capabilities in processing 3D urban scenes for applications such as three-dimensional reconstruction, semantic modeling, and augmented reality. However, research on grottoes scenes is very limited. It is currently unclear how existing neural architectures for point cloud semantic segmentation perform in grotto scenes, and how to effectively incorporate the unique characteristics of grotto scenes to enhance the performance of deep neural networks. This study proposed a method for point cloud semantic segmentation of grotto scenes, combining knowledge with deep learning approaches. The method adopted knowledge to guide the creation of benchmark datasets, the design of a neural network called GSS-Net, and the correction of segmentation errors in the results of deep learning. The results show that the proposed method outperforms four existing mainstream models without the correction of segmentation results. Moreover, a set of ablation studies verified the effectiveness of each proposed module. This method not only improves the accuracy of point cloud semantic segmentation in grotto scenes but also enhances the interpretability of network designs. It provides new insights into the application of knowledge-guided deep learning models in grotto scenes.

The Prevalence of Optical Coherence Tomography Artifacts in High Myopia and its Influence on Glaucoma Diagnosis.

OCT artifacts occur much more frequently in highly myopic eyes compared to non-highly myopic eyes. A longer axial length is predictive of having OCT artifacts. To investigate the types and prevalence of artifacts on optical coherence tomography (OCT) scans in patients with and without high myopia. Patients were divided into 4 groups based on whether they had glaucoma and/or high myopia. All peripapillary retinal nerve fiber layer (RNFL) scan images were individually inspected for the presence of artifacts. Two hundred and twenty-six patients were enrolled. The prevalence of OCT artifacts was 18.6% in non-high myopes and 51.9% in high myopes (P<0.001). Outer RNFL border misidentification was the most common type of artifact for non-high myopes, while retinal pathology-related artifact was the most common in high myopes. Univariable regression analysis showed that a longer axial length (OR 1.815, P<0.001), a higher pattern standard deviation (OR 1.194, P<0.001), and thinner RNFL (OR 0.947, P<0.001) were predictive factors for the presence of OCT artifacts. The diagnostic capability of global RNFL thickness before and after manual correction of segmentation errors did not differ for both non-high myopes (AUC 0.915 to 0.913 [P=0.955]) and high myopes (AUC 0.906 to 0.917 [P=0.806]). The prevalence of OCT artifacts was the highest in patients with both high myopia and glaucoma. The most common type of OCT artifact is different for non-high myopes and high myopes. Physicians need to be aware of a higher likelihood of OCT artifacts, particularly in those with a longer axial length, worse visual field, and thinner RNFL thickness.

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.

Segmentation Error Correction of the Optical Coherence Tomography Angiography Images in Peer-Reviewed Studies.

To assess the percentage of published articles reporting optical coherence tomography angiography (OCTA) metrics regarding the report of segmentation error correction. A comprehensive search was conducted using the PubMed database for articles on OCTA imaging published between January 1, 2015, and January 1, 2021. All original articles reporting at least one of the OCTA metrics were extracted. The article text was reviewed for the segmentation correction strategy. In addition, the number of articles that mentioned the lack of segmentation error correction as a limitation of the study was recorded. From the initial 5288 articles, 1559 articles were included for detailed review. One hundred ninety-six articles (12.57%) used manual correction for segmentation errors. Of the remaining articles, 589 articles (37.8%) excluded images with significant segmentation errors, and 99 articles (6.3%) mentioned segmentation errors as a limitation of their study. The rest of the articles (675, 43.3%) did not address the segmentation error. Multiple logistic regression analysis revealed that ignorance of segmentation error was significantly associated with lower journal ranks, earlier years of publication and disease category of age-related macular degeneration, and glaucoma (all P < 0.001). A significant proportion of peer-reviewed articles in PubMed, disregarded the segmentation error correction. The conclusions of such studies should be interpreted with caution. Editors, reviewers, and authors of OCTA articles should pay special attention to the correction of segmentation errors.

Segmentation Errors in the Measurement of Volumetric Parameters by Swept-Source Anterior Segment Optical Coherence Tomography.

Purpose: To investigate the error rate of segmentation in the automatic measurement of anterior chamber volume (ACV) and iris volume (IV) by swept-source anterior segment optical coherence tomography (SS-ASOCT) in narrow-angle and wide-angle eyes.Methods: In this study, fifty eyes from 25 narrow-angle subjects and fifty eyes from 25 wide-angle subjects were enrolled. SS-ASOCT examinations were performed and each SS-ASOCT scan was reviewed, and segmentation errors in the automatic measurement of ACV and IV were classified and manually corrected. Error rates were compared between the narrow-angle and the wide-angle groups, and ACV and IV before and after manual correction were compared.Results: A total of 12,800 SS-ASOCT scans were reviewed. Segmentation error rates of angle recess, iris root, posterior surface of the iris, pupil margin, and anterior surface of the lens were 84.06, 93.30, 13.15, 59.21, and 25.27%, respectively. Segmentation errors of angle recess, iris root, posterior surface of the iris, and pupil margin occurred more frequently in narrow-angle eyes, while more segmentation errors of the anterior surface of the lens were found in wide-angle eyes (all P < 0.001). ACV decreased and IV increased significantly after manual correction of segmentation errors in both groups (all P < 0.01).Conclusion: Segmentation errors were prevalent in the volumetric measurement by SS-ASOCT, particularly in narrow-angle eyes, leading to mismeasurement of ACV and IV.

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation-including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

APOSTEL 2.0 Recommendations for Reporting Quantitative Optical Coherence Tomography Studies.

ObjectiveTo update the consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results, thus revising the previously published Advised Protocol for OCT Study Terminology and Elements (APOSTEL) recommendations.MethodsTo identify studies reporting quantitative OCT results, we performed a PubMed search for the terms “quantitative” and “optical coherence tomography” from 2015 to 2017. Corresponding authors of the identified publications were invited to provide feedback on the initial APOSTEL recommendations via online surveys following the principle of a modified Delphi method. The results were evaluated and discussed by a panel of experts and changes to the initial recommendations were proposed. A final survey was recirculated among the corresponding authors to obtain a majority vote on the proposed changes.ResultsA total of 116 authors participated in the surveys, resulting in 15 suggestions, of which 12 were finally accepted and incorporated into an updated 9-point checklist. We harmonized the nomenclature of the outer retinal layers, added the exact area of measurement to the description of volume scans, and suggested reporting device-specific features. We advised to address potential bias in manual segmentation or manual correction of segmentation errors. References to specific reporting guidelines and room light conditions were removed. The participants' consensus with the recommendations increased from 80% for the previous APOSTEL version to greater than 90%.ConclusionsThe modified Delphi method resulted in an expert-led guideline (evidence Class III; Grading of Recommendations, Assessment, Development and Evaluations [GRADE] criteria) concerning study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition analysis, nomenclature and abbreviations, and statistical approach. It will be essential to update these recommendations to new research and practices regularly.

사용자의 입력 의도를 반영한 음절 N-gram 기반 한국어 띄어쓰기 및 붙여쓰기 오류 교정 시스템

기존의 자동 띄어쓰기 시스템은 사용자의 띄어쓰기 정보를 활용하지 않고 띄어쓰기를 모두 제거한 문장에 대해 공백을 삽입하는 방식으로 띄어쓰기 오류를 수정한다. 이러한 방식으로 띄어쓰기 오류를 교정할 경우, 사용자가 올바르게 입력한 띄어쓰기를 수정하는 문제와 사용자의 의도를 충분히 반영하지 못하는 문제가 발생한다. 본 논문에서는 이러한 문제를 보완하기 위해 사용자가 입력한 의도를 반영한 음절 N-gram 기반 한국어 띄어쓰기 및 붙여쓰기 오류 교정 시스템을 제안한다. 실험 결과, 오류가 10% 포함된 문장에 대해서 음절 단위 정확률 99.05%, 어절 단위 F1 score 95.57%라는 높은 성능을 보였다. 이는 사용자의 띄어쓰기 정보를 활용하지 않은 기존 방식보다 음절 단위 정확률 1.85%, 어절 단위 F1 score 5.84% 향상된 결과이다. 또한, 딥러닝 방식이 아닌 음절 확률 통계정보만을 사용함으로써 초당 2691.69 문장의 빠른 교정 속도를 보였다.

Customized Slab-Segmentation Method for Projection-Artifact Elimination in Best Vitelliform Macular Dystrophy: A Swept-Source Optical Coherence Tomography Angiography Study

PurposeTo assess the efficacy of customized slab segmentation in eliminating projection artifacts in swept-source optical coherence tomography angiography (SS-OCTA) images of Best vitelliform macular dystrophy (BVMD).MethodsProspective case series including different stages of BVMD. We analyzed SS-OCTA images for flow signals in the outer retina and coregistered B-scan images for distortion of the segmentation slabs defining the outer retina. We applied a customized method for slab realignment whenever BVMD lesions produced distortion of the slabs. Afterward, we checked the images to determine whether the previously noted flow signal had persisted or disappeared, described as “true flow” or “pseudoflow”, respectively. Categorical variables were analyzed with X2 or Fisher’s exact tests, while quantitative variables were analyzed with independent t-test at p<0.05.ResultsThe study included 39 eyes of 22 patients. We detected BVMD patterns I (dome-shaped hyperreflective lesion without neurosensory retinal detachment), II (knob-like hyperreflective lesion with localized neurosensory retinal detachment), and III (heterogeneous scattered hyperreflective material) in 49%, 23%, and 28% of eyes, respectively. Pseudoflow was evident mostly in eyes with pattern II lesions, presence of flow signal within BVMD lesions, and lesions whose height represented >80% of the retinal thickness (p<0.001).ConclusionCustomized slab segmentation is effective in eliminating projection artifact in SS-OCTA images of BVMD.SummaryProjection artifact is a significant confounding factor in emerging SS-OCTA technology through production of pseudoflow signals that can lead to misinterpretation of images of BVMD lesions. The present study proposes a customized method for correction of segmentation errors to eliminate projection artifacts in SS-OCTA images of BVMD patients.

Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors

In most research that has dealt with the correction of spelling errors, the errors are caused by the misuse of space (deletion or insertion of space) are not tackled. Forgetting to deal with this type of errors in the texts poses a problem of understanding and ambiguity of the meaning of the sentence containing these errors. In this article, we propose a new approach to correct errors due to the insertion of space in a word, and at the same time correct other types of editing errors. This approach is based on the edit distance and uses bi-grams language models to correct words in context. The test conducted on hundreds of erroneous words (by insertion of space and/or by simple editing errors) made it possible to assess the relevance and validity of the methods developed to correct this type of error. The approaches proposed in this article provide a very important clarification and reminder by comparing them to those of other existing approaches.

Stepwise segmentation error correction in optical coherence tomography angiography images of patients with diabetic macular edema.

Aim:To determine the minimum number of optical coherence tomography B-scan corrections required to provide acceptable vessel density measurements on optical coherence tomography angiography images in eyes with diabetic macular edema.Methods:In this prospective, noninterventional case series, the optical coherence tomography angiography images of eyes with center-involving diabetic macular edema were assessed. Optical coherence tomography angiography imaging was performed using RTVue Avanti spectral-domain optical coherence tomography system with the AngioVue software (V.2017.1.0.151; Optovue, Fremont, CA, USA). Segmentation error was recorded and manually corrected in the inner retinal layers in the central foveal, 100th and 200th optical coherence tomography B-scans. The segmentation error correction was then continued until all optical coherence tomography B-scans in whole en face image were corrected. At each step, the manual correction of each optical coherence tomography B-scan was propagated to whole image. The vessel density and retinal thickness were recorded at baseline and after each optical coherence tomography B-scan correction.Results:A total of 36 eyes of 26 patients were included. To achieve full segmentation error correction in whole en face image, an average of 1.72 ± 1.81 and 5.57 ± 3.87 B-scans was corrected in inner plexiform layer and outer plexiform layer, respectively. The change in the vessel density measurements after complete segmentation error correction was statistically significant after inner plexiform layer correction. However, no statistically significant change in vessel density was found after manual correction of the outer plexiform layer. The vessel density measurements were statistically significantly different after single central foveal B-scan correction of inner plexiform layer compared with the baseline measurements (p = 0.03); however, it remained unchanged after further segmentation corrections of inner plexiform layer.Conclusion:Multiple optical coherence tomography B-scans should be manually corrected to address segmentation error in whole images of en face optical coherence tomography angiography in eyes with diabetic macular edema. Correction of central foveal B-scan provides the most significant change in vessel density measurements in eyes with diabetic macular edema.

Effect of segmentation error correction on optical coherence tomography angiography measurements in healthy subjects and diabetic macular oedema

PurposeTo evaluate the impact of segmentation error on vessel density measurements in healthy eyes and eyes with diabetic macular oedema (DMO).MethodsIn this prospective, comparative, non-interventional study, enface optical coherence tomography...

Retinal Layer Thicknesses in Early Age-Related Macular Degeneration: Results From the German AugUR Study.

PurposeTo systematically analyze thicknesses of retinal layers in an older population and their link to early age-related macular degeneration (AMD).MethodsIn the AugUR baseline survey from a population aged ≥70 years, we conducted multimodal retinal imaging, including spectral-domain optical coherence tomography. Autosegmentation of eight distinct retinal layers was followed by manual correction of segmentation errors. AMD status was graded on color fundus images according to the Three Continent AMD Consortium Severity Scale. We tested the association of early AMD on retinal layer thicknesses by using linear mixed models and replicated significant results in independent data also from the AugUR platform.ResultsWhen comparing layer thicknesses between early AMD and no AMD (822 eyes, 449 participants), the retinal pigment epithelium/Bruch's membrane complex demonstrated a statistically significant thickening (e.g., P = 6.41 × 10−92 for severe early versus no AMD) and photoreceptor layers showed a significant thinning. Autosegmented retinal layer thicknesses revealed similar associations as manually corrected values but underestimated some effects. Independent replication analysis in 1026 eyes (546 participants) confirmed associations (e.g., P = 9.38 × 10−36 for retinal pigment epithelium/Bruch's membrane complex, severe early versus no AMD).ConclusionsThis first population-based study on spectral-domain optical coherence tomography-derived retinal layer thicknesses in a total of ∼1000 individuals provides insights into the reliability of autosegmentation and layer-specific reference values for an older population. Our findings show a difference in thicknesses between early AMD and no AMD for some retinal layers, suggesting these as potential imaging biomarkers. The thinning of photoreceptor layers substantiates a photoreceptor cell loss/damage already occurring in early AMD.

Mereotopological Correction of Segmentation Errors in Histological Imaging

In this paper we describe mereotopological methods to programmatically correct image segmentation errors, in particular those that fail to fulfil expected spatial relations in digitised histological scenes. The proposed approach exploits a spatial logic called discrete mereotopology to integrate a number of qualitative spatial reasoning and constraint satisfaction methods into imaging procedures. Eight mereotopological relations defined on binary region pairs are represented as nodes in a set of 20 directed graphs, where the node-to-node graph edges encode the possible transitions between the spatial relations after set-theoretic and discrete topological operations on the regions are applied. The graphs allow one to identify sequences of operations that applied to regions of a given relation, and enables one to resegment an image that fails to conform to a valid histological model into one that does. Examples of the methods are presented using images of H&amp;E-stained human carcinoma cell line cultures.

CellECT: cell evolution capturing tool.

BackgroundRobust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance. Identification and correction of segmentation errors in time-series data is of prime importance for proper validation of the subsequent analysis. The primary contribution of this work is a novel method for interactive segmentation and analysis of microscopy data, which learns from and guides user interactions to improve overall segmentation.ResultsWe introduce an interactive cell analysis application, called CellECT, for 3D+t microscopy datasets. The core segmentation tool is watershed-based and allows the user to add, remove or modify existing segments by means of manipulating guidance markers. A confidence metric learns from the user interaction and highlights regions of uncertainty in the segmentation for the user’s attention. User corrected segmentations are then propagated to neighboring time points. The analysis tool computes local and global statistics for various cell measurements over the time sequence. Detailed results on two large datasets containing membrane and nuclei data are presented: a 3D+t confocal microscopy dataset of the ascidian Phallusia mammillata consisting of 18 time points, and a 3D+t single plane illumination microscopy (SPIM) dataset consisting of 192 time points. Additionally, CellECT was used to segment a large population of jigsaw-puzzle shaped epidermal cells from Arabidopsis thaliana leaves. The cell coordinates obtained using CellECT are compared to those of manually segmented cells.ConclusionsCellECT provides tools for convenient segmentation and analysis of 3D+t membrane datasets by incorporating human interaction into automated algorithms. Users can modify segmentation results through the help of guidance markers, and an adaptive confidence metric highlights problematic regions. Segmentations can be propagated to multiple time points, and once a segmentation is available for a time sequence cells can be analyzed to observe trends. The segmentation and analysis tools presented here generalize well to membrane or cell wall volumetric time series datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0927-7) contains supplementary material, which is available to authorized users.

Outer Retinal Tubulation as a Predictor of the Enlargement Amount of Geographic Atrophy in Age-Related Macular Degeneration

To determine the prognostic value of outer retinal tubulation (ORT) in the enlargement amount of geographic atrophy (GA) in eyes with age-related macular degeneration (AMD). Cohort study. One hundred eight fellow untreated eyes of 143 patients with GA resulting from AMD enrolled in the MAHALO study (clinicaltrials.gov identifier, NCT01229215) who completely satisfied the study term and had gradable spectral-domain optical coherence tomography (OCT) images obtained at both baseline and month 18 visits. The MAHALO study enrolled 143 subjects into a phase 1b/2 multicenter, randomized, single-masked, sham-injection controlled clinical trial of the safety, tolerability, and evidence of activity of lampalizumab in patients with GA associated with AMD. Spectral-domain optical coherence tomography images were obtained at multiple time points in both eyes, although only the baseline and month 18 data of the fellow (nonstudy) eyes were considered in this exploratory analysis. The Cirrus HD-OCT review software was used for automatic segmentation and measurement of GA areas, with manual correction of segmentation errors by certified OCT graders. Baseline OCT images also were assessed for the presence of ORT. The enlargement amount of GA in eyes with ORT was compared with that of eyes without ORT. Comparison of the enlargement amount of GA in eyes with and without ORT. Twenty-four of these 108 eyes demonstrated evidence of ORT. The amount of enlargement of GA in eyes with ORT was significantly slower than that of eyes without ORT (1.85±0.78 vs. 2.67±1.61; P= 0.001). This difference remained significant when considering subgroups with unifocal or multifocal GA lesions, because eyes with ORT in both subgroups had a slower enlargement amount of GA than eyes without ORT (2.91±1.70 vs. 2.08±0.88 [P= 0.01], in eyes with multifocal GA lesions; and 2.24±1.40 vs. 1.63±0.57 [P= 0.02], in eyes with unifocal GA lesions). In eyes with ORT, GA lesions seem to enlarge at a significantly slower rate than those of eyes without ORT. The presence of ORT may need to be accounted for in longitudinal studies of GA.

Automated volumetric analysis of interface fluid in descemet stripping automated endothelial keratoplasty using intraoperative optical coherence tomography.

We demonstrated a novel automated algorithm for segmentation of intraoperative optical coherence tomography (iOCT) imaging of fluid interface gap in Descemet stripping automated endothelial keratoplasty (DSAEK) and evaluated the effect of intraoperative maneuvers to promote graft apposition on interface dimensions. A total of 30 eyes of 29 patients from the anterior segment arm of the PIONEER study was included in this analysis. The iOCT scans were entered into an automated algorithm that delineated the spatial extent of the fluid interface gap in three dimensions between donor and host cornea during surgery. The algorithm was validated against manual segmentation, and performance was evaluated by absolute accuracy and intraclass correlation coefficient. Patients underwent DSAEK using a standard sequence of maneuvers, including controlled elevation of IOP and compressive corneal sweep to promote graft adhesion. Measurement of interface fluid volume, en face area, and maximal interface height were compared between scans before anterior chamber infusion, after pressure elevation alone, and after corneal sweep with pressure elevation using dependent-samples t-test. The algorithm achieved 87% absolute accuracy and an intraclass correlation of 0.96. Nine datasets of a total of 84 (11%) required human correction of segmentation errors. Mean interface fluid volume was significantly decreased by corneal sweep (P = 0.021) and by both maneuvers combined (P = 0.046). Mean en face area was significantly decreased by corneal sweep (P = 0.010) and the maneuvers combined (P < 0.001). Maximal interface height was significantly decreased by pressure elevation (P = 0.010), corneal sweep (P = 0.009), and the maneuvers combined (P = 0.010). Quantitative analysis of iOCT volumetric scans shows the significant effect of controlled pressure elevation and corneal sweep on graft apposition in DSAEK. Computerized iOCT analysis yields objective measurements of interface fluid intraoperatively, which provides information on anatomic outcomes and could be used in future trials.

Reading Center Characterization of Central Retinal Vein Occlusion Using Optical Coherence Tomography During the COPERNICUS Trial

To determine the impact of segmentation error correction and precision of standardized grading of time domain optical coherence tomography (OCT) scans obtained during an interventional study for macular edema secondary to central retinal vein occlusion (CRVO). A reading center team of two readers and a senior reader evaluated 1199 OCT scans. Manual segmentation error correction (SEC) was performed. The frequency of SEC, resulting change in central retinal thickness after SEC, and reproducibility of SEC were quantified. Optical coherence tomography characteristics associated with the need for SECs were determined. Reading center teams graded all scans, and the reproducibility of this evaluation for scan quality at the fovea and cystoid macular edema was determined on 97 scans. Segmentation errors were observed in 360 (30.0%) scans, of which 312 were interpretable. On these 312 scans, the mean machine-generated central subfield thickness (CST) was 507.4 ± 208.5 μm compared to 583.0 ± 266.2 μm after SEC. Segmentation error correction resulted in a mean absolute CST correction of 81.3 ± 162.0 μm from baseline uncorrected CST. Segmentation error correction was highly reproducible (intraclass correlation coefficient [ICC] = 0.99-1.00). Epiretinal membrane (odds ratio [OR] = 2.3, P < 0.0001), subretinal fluid (OR = 2.1, P = 0.0005), and increasing CST (OR = 1.6 per 100-μm increase, P < 0.001) were associated with need for SEC. Reading center teams reproducibly graded scan quality at the fovea (87% agreement, kappa = 0.64, 95% confidence interval [CI] 0.45-0.82) and cystoid macular edema (92% agreement, kappa = 0.84, 95% CI 0.74-0.94). Optical coherence tomography images obtained during an interventional CRVO treatment trial can be reproducibly graded. Segmentation errors can cause clinically meaningful deviation in central retinal thickness measurements; however, these errors can be corrected reproducibly in a reading center setting. Segmentation errors are common on these images, can cause clinically meaningful errors in central retinal thickness measurement, and can be corrected reproducibly in a reading center setting.

Effect of OCT volume scan density on thickness measurements in diabetic macular edema

To evaluate the impact of reducing B-scan frame-sampling density on retinal thickness measurements using spectral domain optical coherence tomography (SD-OCT) in eyes with diabetic macular edema (DME). We retrospectively collected OCT data for 64 eyes of 43 patients undergoing imaging for DME using the Cirrus HD-OCT 512 × 128 macular cube protocol. For each case, raw OCT data were imported into the 3D-OCTOR software, and retinal thickness maps were generated using all 128 B-scans and for lower densities of B-scans ranging from every other scan to only four scans (every 30-s B-scan). Maps were generated before and after manual correction of retinal boundary segmentation errors. The foveal central subfield (FCS) and total macular volume (TMV) values were used to compare thickness maps of varying densities. The mean difference in FCS retinal thickness and TMV increased as the B-scan density was reduced, particularly when the density was reduced to fewer than 16 B-scans over 6 mm. At a density of 16 B-scans, the mean absolute difference in FCS thickness was 2.43 μm (0.79%), with a maximum of 10.1 μm (4.09%). At this density, the mean difference in TMV was 0.012 mm(3) (0.13%), with a maximum difference of 0.04 mm(3) (0.47%). Manual correction of OCT segmentation errors resulted in a difference in FCS thickness of ≥ 10 μm in only 12.5% of cases, with a maximum difference of 115.7 μm. A minimum of 16 equally spaced B-scans appear necessary to generate retinal thickness measurements similar to those produced using all 128 B-scans in eyes with DME. Manual correction of segmentation errors appeared to have a clinically meaningful effect in a small minority of cases. These results may have implications for the design of SD-OCT imaging and grading protocols in clinical trials of DME, particularly when using multiple SD-OCT instruments that acquire varying numbers of B-scans.

Reproducibility of segmentation error correction in age-related macular degeneration: Stratus versus Cirrus OCT

IntroductionThe accuracy of retinal thickness measurement in age-related macular degeneration by optical coherence tomography (OCT) is affected by threshold algorithm line errors. The reproducibility of error correction in Stratus and...